论文标题
在磁通量下的两腿玻色梯子梯子的神经网络量子状态
Neural-network quantum states for a two-leg Bose-Hubbard ladder under magnetic flux
论文作者
论文摘要
量子气体系统是理想的模拟量子模拟平台，可解决在密切相关的量子物质中最具挑战性的问题。但是，他们还暴露了对新理论框架的迫切需求。一维中的简单模型，通过常规方法进行了良好的研究，最近已有大量注意作为新方法的测试用例。梯子模型提供了合理的下一步，其中建立的数值方法仍然可靠，但是可以引入更高维效应（例如仪表场）的并发症。在本文中，我们研究了最近开发的神经网络量子态在强合成磁场下在两腿玻色梯子阶梯中的应用。基于受限的玻尔兹曼机器和进发神经网络，我们表明，变异神经网络可以可靠地预测强耦合极限的超氟莫特绝缘体相图，可与密度 - 矩阵重质量分配组的准确性相当。在弱耦合极限中，神经网络还诊断出其他多体现象，例如涡流，手性和偏置阶段。我们的工作表明，具有磁通量的两腿玻色bard模型是神经网络量子状态未来发展的理想测试场。
Quantum gas systems are ideal analog quantum simulation platforms for tackling some of the most challenging problems in strongly correlated quantum matter. However, they also expose the urgent need for new theoretical frameworks. Simple models in one dimension, well studied with conventional methods, have received considerable recent attention as test cases for new approaches. Ladder models provide the logical next step, where established numerical methods are still reliable, but complications of higher dimensional effects like gauge fields can be introduced. In this paper, we investigate the application of the recently developed neural-network quantum states in the two-leg Bose-Hubbard ladder under strong synthetic magnetic fields. Based on the restricted Boltzmann machine and feedforward neural network, we show that variational neural networks can reliably predict the superfluid-Mott insulator phase diagram in the strong coupling limit comparable with the accuracy of the density-matrix renormalization group. In the weak coupling limit, neural networks also diagnose other many-body phenomena such as the vortex, chiral, and biased-ladder phases. Our work demonstrates that the two-leg Bose-Hubbard model with magnetic flux is an ideal test ground for future developments of neural-network quantum states.
